ABSTRACT
We use hierarchical cluster analysis, principal component analysis, multi-dimensional scaling and discriminant analysis to investigate the internal representations learnt by a recent connectionist model of reading aloud. The learning trajectories of these representations may help us understand reading development in children and the results of naming latency experiments in adults. Studying the effects of network damage on these representations seems to provide insight into the mechanisms underlying acquired surface dyslexia. The discussion of the various techniques used may also prove useful in analysing the functioning of other connectionist systems.
